code: def match_train_config(predict_ds, train_ds):
      # It copies the important configurations from the train dataset of the model
      # into the predict_ds to be used for prediction. It is needed to match the training configurations.
      if train_ds is None:
          return
  
      predict_ds.sample_rate = train_ds.get("sample_rate", 16000)
      cfg_name_list = [
          "int_values",
          "use_start_end_token",
          "blank_index",
          "unk_index",
          "normalize",
          "parser",
          "eos_id",
          "bos_id",
          "pad_id",
      ]
  
      if is_dataclass(predict_ds):
          predict_ds = OmegaConf.structured(predict_ds)
      for cfg_name in cfg_name_list:
          if hasattr(train_ds, cfg_name):
              setattr(predict_ds, cfg_name, getattr(train_ds, cfg_name))
  
      return predict_ds
  
file_path: NeMo/examples/asr/transcribe_speech_parallel.py
imports: import itertools
  import json
  import os
  from dataclasses import dataclass, is_dataclass
  from typing import Optional
  import pytorch_lightning
  import torch
  from omegaconf import MISSING, OmegaConf
  from nemo.collections.asr.data.audio_to_text_dataset import ASRPredictionWriter
  from nemo.collections.asr.metrics.wer import word_error_rate
  from nemo.collections.asr.models import ASRModel, EncDecHybridRNNTCTCModel
  from nemo.collections.asr.models.configs.asr_models_config import ASRDatasetConfig
  from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecodingConfig
  from nemo.core.config import TrainerConfig, hydra_runner
  from nemo.utils import logging
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 1 for calling a global function with example usage: match_train_config(predict_ds, train_ds) and returns: predict_ds